LlamaCloud Index v2 — Agentic Retrieval Guide

Answer questions from documents stored in Index v2, the managed knowledge base of the LlamaParse Platform. Every operation is a single authenticated HTTP call, so from a shell the cheapest interface is curl + jq: you can compose calls, batch independent lookups into one command, and bound exactly how much output enters the conversation.

Index v2 is built for agentic retrieval: beyond semantic search, it gives you file-system-like access to the underlying documents (PDFs, Office documents, images, and other unstructured files). This skill is about using that access well — navigating the index the way you would a file system, instead of firing a single one-shot RAG query and hoping the top results contain the answer.

Setup

export LLAMA_BASE="https://api.cloud.llamaindex.ai/api/v1"   # EU accounts: https://api.cloud.eu.llamaindex.ai/api/v1
AUTH=(-H "Authorization: Bearer $LLAMA_CLOUD_API_KEY" -H "Content-Type: application/json")

If a call returns 401/403, ask the user to check LLAMA_CLOUD_API_KEY (and the region) — do not retry in a loop.

The API